Historical analysis of accidents in the Saudi Arabian chemical industry
Bibliographic record
Abstract
Abstract The chemical industry oversees the transformation of raw materials into products through unit operations that require appropriate organization to avoid accidents. Hence, it is important to do analyses to identify any possible mistakes, substances involved, or common sources of accidents in the industry to avoid such errors and design better safety measures to create a safer space for the chemical industry, which is hugely important and boasts a worldwide presence. This document presents an analysis of chemical industry‐related accidents in Saudi Arabia, namely fires, explosions, and toxic clouds which occurred in the chemical and petrochemical industries and while transporting hazardous materials in the last 46 years. Three databases—one for each type of accident—were created with information collected from articles, newspapers, videos, and papers. ‘Explosion’, ‘fire’, and ‘toxic clouds’ were the key words used for the research, focusing on accident taking place in Saudi Arabia. Once the information had been collected, the accidents were filtered, checked, and moved to a fourth general database. It is shown that 54.0% of all related accidents were fires, 25.4% toxic clouds, and 20.6% were explosions. The provinces with the most registered accidents were Riyadh (15), Jeddah (10), and Jubail (6).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".